Being prepared for an interview, with the kind of questions that you might be asked is a great way to ace an interview. If you know the questions and are prepared to answer those questions in the best possible way, you have won the battle. No matter the experience you have, it is a good practice that you go through the possible data engineer interview questions. Ideally, interviewers would want you to think on the fly and check your answers, but not many people are good at communicating well enough what they already know.
If are prepared with the kind of questions that you might be asked, you gain a certain level of confidence during the interview, which again positively impacts your chances of getting through successfully. There are generic interview questions which try to know how you present yourself, what is your thought process, your attitude towards co-workers and working environment and your work ethic. Then there are domain-specific questions that are targeted at the kind of role the interview is being conducted for. Today let’s go through such questions for the role of a Data Engineer.
One important point before we get on with the questions is the difference between Data Engineer and Data Scientist. There are many times, where interview candidates confuse between the 2.
Data Engineers and Data Scientists work closely on Big Data projects and thus there is an apparent overlap between the two roles. The two roles are distinct in their core responsibilities although they work together to achieve a common main objective.
Data Scientists are consumers of the data infrastructure that the Data Engineers build and maintain. Data Engineers ensure that the system is robust and well equipped to handle and process huge amounts of data while being efficient.
With that out of the way, let’s get straight to the questions.
This question is asked in almost every interview, with the intension of understanding how deep does your passion run for data engineering, which makes you get through your challenges every day. You may want to share your story about where you started, where you got hooked onto the role, how you upskilled to gain knowledge about the field and what challenges you overcame to get knowledge or any experience that you might have in data engineering.
Another fundamental question, you can answer by pointing out some exciting features of the role and the job involved, and the kind of work the company is doing in that field that motivates you to join the company. Highlight your qualification, experience, skills and personality to show how all the experience you have gained will help you be a better Data Engineer.
Skills that are essential for a data engineer are
This question is to check if you have understood the role and whether you have a holistic view or a confined understanding. You could start by saying what is known about data engineering in textbooks and then add your own experience or views.
Data engineer’s setup and maintain the infrastructure that supports the information infrastructure and related applications. Data engineer’s role has been carved out from a core IT role after the middle layer in information systems within businesses started growing manifold. To maintain a big data architecture, you need people who understand data, data ingestion, extraction, transformation, data loading and more, which is more data specific and far removed from core IT practices and yet not sophisticated enough to handle data mining, identifying patterns in data, recommend data-backed changes to the business leadership, which is what data scientists do. So, data engineers are a crucial link between core IT and data scientists.
Data modelling is a scientific way of documenting complex data systems by way of a diagram to give a pictorial and conceptual representation of the system. You could also expand on any experience that you have had with data modelling.
There are mainly two types of schemas in data modelling: 1) Star schema and 2) Snowflake schema. Expand on each or any one of them that you are asked to explain.
Data Engineers constantly work with data that is coming into the systems in all sorts of formats. Broadly categorizing them as structured and unstructured. They differ in the way these are stored and accessed. For your convenience, some of the differences are listed.
| Criteria | Structured Data | Unstructured Data |
| Storage | DBMS | Unmanaged file structures |
| Standard | ADO.net, ODBC, and SQL | STMP, XML, CSV, and SMS |
| Integration Tool | ELT (Extract, Transform, Load) | Manual data entry or batch processing that includes codes |
| scaling | Schema scaling is difficult | Scaling is very easy. |
This is an important question and you should be thorough with your answer as it assesses your understanding of the role and how much you have invested in learning them. You should include the below points in your response.
This question is asked to assess your knowledge of the systems from the ground up. There is no perfect answer to this question and no answer is bad. Responses to the below questions might give you a good answer.
Once these questions are answered, you try to map to the available technologies to address the challenges and characteristics of each. This is not an exhaustive list of questions but is an approach that you can take to respond to the original question from the interviewer.
The algorithm that you select to discuss must be one that you are good at and preferably used by the company. There will be follow up questions to understand the depth of your answer, like,
Be sure to include the challenges that prop up when transforming from unstructured to structured in your response.
There is a good likely hood of this question being asked if you are an experienced candidate. Do mention the tools used for building the model and small brief about how you’ve done it.
Talk about the tool you have used and highlight some of its features that helped you pick it for ETL.
Big Data is a phenomenon, a result of exponential growth in data availability, storage technology and processing power, while Hadoop is a framework that helps to handle huge volumes of data that reside in the Big Data ecosystem. Describe the components of Hadoop as below.
NameNodes store metadata of all the files stored on the cluster. Basically, metadata about data nodes, bits of information like the location of blocks, size of files, hierarchy. It is similar to a File Allocation Table (FAT), which stores information about blocks of data that make up files and where they are stored on a single computer. NameNodes keep the same kind of information for a distributed file system. Under normal circumstances, NameNode crash will result in non-availability of data, although all blocks of data are intact. A high availability setup will ensure there is a passive NameNode that backs up the primary one and takes over in case the NameNode fails.
Blocks are the smallest unit of data allocated to a file, which the Hadoop system automatically creates for storage in different nodes in a distributed file system. Block Scanner verifies the integrity of a DataNode by checking the data blocks stored on it.
A few other questions that are asked in the interviews, that you must be prepared for are listed below.
Q18) Which tools did you pick up for your projects and why?
Q19) What is MapReduce in Hadoop, what role does Reducer play?
Q20) Talk us through how a Big Data solution is deployed.
Q21) What is the approach you will take to deal with duplicate data points?
Q22) What is your experience of Big Data in a cloud environment?
Q23) How can Data Analytics and Big Data help to positively impact the bottom line of the company?
Q24) What is the replication factor in HDFS?
Q25) Explain Block and Block Scanner in HDFS.
Q26) What sequence of events takes place when Block Scanner detects a problem with a data block?
Q27) What messages are transacted between NameNode and DataNode?
Q28) What are the security features in Hadoop?
Q29) Explain Heartbeat in Hadoop.
Q30) What is the difference between NAS and DAS?
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